EP1624411A2 - Filtrage de tissus mous dans des images médicales - Google Patents

Filtrage de tissus mous dans des images médicales Download PDF

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Publication number
EP1624411A2
EP1624411A2 EP04018747A EP04018747A EP1624411A2 EP 1624411 A2 EP1624411 A2 EP 1624411A2 EP 04018747 A EP04018747 A EP 04018747A EP 04018747 A EP04018747 A EP 04018747A EP 1624411 A2 EP1624411 A2 EP 1624411A2
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Prior art keywords
image
histogram
features
category
image elements
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German (de)
English (en)
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EP1624411A3 (fr
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Alberto N. Dipartimento di Scienza Borghese
Iuri Dipartimento di Scienza dell' Frosio
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Gendex Corp
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Gendex Corp
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Priority to US11/196,017 priority patent/US20060029183A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/501—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the head, e.g. neuroimaging or craniography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Definitions

  • the present invention concerns the field of radiographic imaging and in particular the field of processing a radiographic image in order to enhance the visibility of the features shown therein.
  • the present invention will be explained using cephalic X-ray pictures as an example. Although some embodiments of the invention are specifically tailored to the processing of cephalic X-ray pictures, the present invention in general terms is not limited to such embodiments.
  • Cephalic radiographs are widely used by dentists, surgeons and maxillofacial radiologists for providing data on which diagnosis, surgical-planning and implant evaluation may be based. Thanks to modern digital radiographic systems, the qualitative evaluation becomes available in real-time, and the quantitative measurement and visualization of anatomical features (e.g. nasal spine, chin tip, ...), alterations in the patient's anatomy and visualization of post-operative aesthetic modifications can be automatically computed and visualized.
  • anatomical features e.g. nasal spine, chin tip, .
  • radiograms are generally processed to obtain optimal gray level coding, using a variety of techniques, which are generally termed image enhancement techniques.
  • image enhancement techniques show an example of the application of image processing techniques for enhancing digital X-ray images.
  • a general overview of a number of known image enhancement techniques is given in the book Digital Image Processing and Analysis by B. Chanda and D. D. Majumder, Prentice-Hall of India Ptv. Ltd., New Delhi, 2000.
  • image enhancement techniques are used in the field of the present invention, it would be desirable to obtain interactive image processing rates (processing time ⁇ 1 s) for images that are presently of the order of 4 - 5 Mpixels and may comprise even more data in the future.
  • UM Unsharp masking
  • Fig. 1d shows an example of the effect of an unsharp masking operation according to the prior art, which has been applied to the X-ray image shown in Fig. 1a.
  • Unsharp masking can be implemented for working in real time, but it enhances only the small features of the image and increases the noise. Moreover, it does not allow recovering underexposed images, where the dynamics of the gray levels of bone tissue is compressed: the amplitude of high frequencies in the corresponding regions is too small to become clearly visible without adding strong edge artefacts.
  • UM identifies bone structures well, but it cannot recover the soft tissue boundary if the transition between the soft tissue and the background is smooth (large scale); this is critical for instance for the chin tip or nose profile.
  • Scale-space processing Another approach to image enhancement is known as scale-space processing, see, for example, the article "Mammographic feature enhancement by multiscale feature analysis” by Adrew F. Laine, Sergio Schuler, Jian Fan and Walter Huda, IEEE Transactions on Medical Images, Vol. 13, No. 4, December 1994, pp. 725-740.
  • Scale-space processing extends the capabilities of detecting features of different size but does not solve the UM problems, especially when large structures are present as in cephalic images.
  • a further approach to image enhancement is based on re-mapping the gray levels of an image such that the image dynamic for both soft tissue and bone is maximized.
  • a technique called gamma correction is often used in practice because gamma correction can be implemented in real-time.
  • a gamma value of 0,25 which is the usual setting in many clinical applications, makes bone features clearly visible, but soft tissue darkens and tends to mix with the background; an example of this effect is shown in Fig. 1b.
  • Gamma values larger than 1 can be used to recover overexposed soft tissue, but such a setting compresses bone dynamic.
  • Histogram equalization produces results that are generally similar to those obtained by gamma correction with a gamma value ⁇ 1 (see Fig. 1c).
  • DE 199 33 776 A1 discloses an X-ray apparatus wherein the radiation of an X-ray source is controlled by means of a feedback circuit when taking an X-ray panoramic image.
  • the intended effect is that the mean values of the successive columns of the image should be kept constant or should follow another predetermined curve.
  • the document further discloses an embodiment in which a corresponding effect is realized by digital image processing.
  • the present invention has the object of providing a technique that enhances the visibility of features in a radiographic image at least for some of the features that are shown in the image.
  • the invention should also be usable to enhance underexposed and/or overexposed images.
  • the invention comprises a method as defined in claim 1, an apparatus as defined in claim 22, and a computer-readable data carrier as defined in claim 23.
  • the invention is based on the idea to enhance the visibility of at least some features of a radiographic image, the features belonging to at least a first and a second category of features, by determining a histogram of the image, analyzing the histogram in order to determine a distinction between values of image elements that more likely show a feature of the first category and values of image elements that more likely show a feature of the second category, and applying a correction to at least some of the image elements, wherein an image element that, according to the determined distinction, more likely shows a feature of the first category is corrected differently than an image element that, according to the determined distinction, more likely shows a feature of the second category.
  • the invention may be used for processing medical X-ray images, in particular digital cephalic radiographies, so that both soft tissue features and bone features are made clearly visible under a wide range of exposures, including underexposure of bone and overexposure of soft tissue.
  • medical X-ray images in particular digital cephalic radiographies
  • both soft tissue features and bone features are made clearly visible under a wide range of exposures, including underexposure of bone and overexposure of soft tissue.
  • the first category of features comprises features of soft tissue shown in the image
  • the second category of features comprises features of bone shown in the image.
  • the visibility of the features of both of these categories is enhanced, but it may also be advantageous for some applications to provide parameter settings in which only soft tissue features or only bone features are emphasized.
  • a third category of features may comprise features of the background shown in the image. It is preferred that such background features are suppressed - or at least not particularly enhanced - in some embodiments of the invention.
  • irregular image elements - e.g., image elements near a border and/or very bright and/or very dark image elements - are disregarded when calculating the histogram.
  • a model histogram is generated and fitted to the actual histogram of the image in a histogram analysis part of the image enhancement process.
  • the model histogram may comprise a plurality of components, each of which preferably corresponding to one category or several categories of features shown in the image. For example, there may be a component that represents the category of soft tissue, a component that represents the category of bone, and so on.
  • the model histogram may be formed according to a mixture model, and each of the components may be a statistical distribution.
  • the model histogram is composed of a number of segments, each segment corresponding to one component.
  • Each segment may, for example, be a straight line segment or a part of a parabola. It is preferred that each such segment is defined by a simple mathematical equation, e.g., a linear or quadratic or cubic equation.
  • the process of fitting the model histogram to the actual histogram preferably maximizes the likelihood of the observed data.
  • An iterative approximation process may be used in some embodiments.
  • histogram analysis produces at least one boundary value that marks the boundary between two categories of features.
  • the correction applied to each image element is preferably influenced by the distinction of whether the value of this image element is below or above the boundary value.
  • the correction may comprise a gamma correction with at least two different gamma correction values.
  • the correction may comprise a linear stretching and/or a correction of saturation.
  • the gamma correction values that are used in the image correction step may, in some embodiments, be smoothed spatially in order to avoid artifacts in the corrected image.
  • a two-dimensional look-up table may be used to speed up the correction process.
  • each image element that is processed according to the present invention is an individual pixel.
  • the image elements represent clusters of pixels or have been generated from the original image in a pre-processing step.
  • the value of each image element is preferably the gray level of the corresponding pixel.
  • different notions of the value of an image element e.g., the mean gray level of a cluster of pixels or a value that emphasizes certain kinds of information contained in the image may be used.
  • the apparatus of the present invention may, for example, be a digital X-ray apparatus or an image processing apparatus or any other data processing apparatus, including a suitably programmed personal computer or workstation.
  • the computer-readable data carrier may be, without limitation, a material data carrier like, e.g., a hard disk or a CD-ROM or a semiconductor memory, or an immaterial data carrier like, e.g., a carrier wave or a signal transmitted through a computer network.
  • the apparatus and the data carrier comprise features that correspond to the features set forth in the present description and/or in the dependent method claims.
  • Fig. 1a depicts a typical cephalic radiographic image having a size of 1871 x 2605 pixels.
  • Fig. 1c shows the image of Fig. 1a after a histogram equalization step according to the prior art. Both histogram equalization and global gamma correction enhance the pixels that show bone features, but the soft tissue remains dark and tends to mix with the background.
  • Fig. 1d The image shown in Fig. 1d has been obtained from the image of Fig. 1a by means of an Unsharp Masking operation. It is apparent that the high frequencies are enhanced, but noise is increased, whereas bone remains not clearly visible.
  • Fig. 2a shows a flow diagram of the method of the present invention in a first sample embodiment.
  • the method can be divided into three parts, each part comprising one or more method steps. It is to be understood that the parts and steps of the method do not necessarily have to be performed in a strictly sequential fashion. In fact, embodiments are contemplated in which the parts and/or steps are performed in an at least partially parallel or an at least partially interleaved fashion.
  • step 10 of the present embodiment, by taking out saturated pixels and pixels that belong to borders or logo elements.
  • the histogram is then calculated in step 12 without taking such irregular pixels into account. It is to be understood that, in typical implementations of the present invention, step 10 will be performed partly in conjunction with step 12 and partly as a post-processing step on the histogram obtained in step 12.
  • the second part of the method namely histogram analysis, has the object of determining a distinction between different categories of features contained in the image.
  • three such categories are used, namely background, soft tissue and bony tissue. These categories correspond to three components of the histogram.
  • a model of the histogram is generated in step 14.
  • the histogram is modeled by a mixture (e.g., a weighted linear combination) of one model distribution for each component of the histogram.
  • a mixture e.g., a weighted linear combination
  • two Gaussian distributions are used to model the background and soft tissue components of the histogram, respectively, and a Lognormal distribution is used to model the bony tissue component of the histogram.
  • a boundary value (e.g., a threshold for the individual pixel values of the image) is determined in step 18. The boundary value serves to distinguish pixels that likely belong to soft tissue features from pixels that likely belong to bony tissue features.
  • the third part of the method concerns the actual image correction.
  • the boundary value determined in step 18 is used in step 20 to build a gamma correction map that contains a desired gamma correction value for each pixel.
  • This gamma correction map is smoothed in step 22.
  • the smoothed map is applied to the original image in step 24.
  • Step 24 may be performed by applying a correction formula to each pixel in the image to be processed, the correction formula taking into account the respective gamma correction value for this pixel as defined in the smoothed gamma correction map.
  • a two-dimensional look-up table may be calculated for the image to speed up the image correction process.
  • Fig. 2a uses a rather sophisticated model in which the modeled histogram is a mixture of several statistical distributions. This makes it necessary to employ an iterative approximation routine for adjusting the model parameters in steps 14 and 16.
  • Fig. 2b shows an alternative embodiment of the histogram analysis part of the method.
  • a simplified model is used, wherein the modeled histogram consists of segments that are defined by comparatively simple mathematical equations, e.g., equations that define straight line segments or parts of a parabola.
  • the fitting of this model histogram to the actual histogram can then be performed in a simplified and very efficient process, which may or may not require iterative approximation. This process is shown in Fig. 2b as step 26.
  • the boundary value resulting from step 26 is one of the parameters of the model histogram, namely the gray level coordinate (abscissa) of the point where the segment of the histogram that models the soft tissue features goes into the segment of the histogram that models the bone features.
  • Sections 1, 2 and 4 describe the method shown in Fig. 2a, and section 3 covers the variant of the histogram analysis part that has been shown in Fig. 2b. It is to be understood that the details presented below only illustrate certain specific embodiments of the present invention, and should not be construed as limitations of the scope of protection.
  • a typical histogram of a cephalic radiographic image is shown in Fig. 3a.
  • This histogram has a consistent shape with six well-defined peaks.
  • Peak 1 is associated with saturated CCD pixels (corresponding to a gray level equal to 0).
  • Peaks 2 and 3 represent the image background; the reason for the presence of a double peak is the use of Automatic Exposure Control (AEC), which has been introduced in the latest generation of radiographic equipment to limit the soft-tissue overexposure in the frontal part of the face of the patient.
  • Peak 4 is associated with the bone structures; it is asymmetric and shows a larger slope for the highest gray levels.
  • Peak 5 corresponds to pixels on the border of the CCD sensor, which receive almost no X-rays.
  • Peak 6 is associated with the digital logo printed on the radiography (corresponding to the maximum gray level, equal to N GL -1, where N GL is the total number of gray level values, e.g., 256 in the present sample embodiments).
  • H 1F a working histogram of the image, H 1F , is computed using only the remaining pixels.
  • the resulting histogram H 1F is low pass filtered.
  • Fig. 3b shows a diagram in which eighteen low pass filtered histograms have been plotted, one histogram for each of eighteen typical lateral cephalic radiographies. It is apparent that only peaks 2, 3 and 4 are present in H 1F .
  • the probability that a certain gray level, x, appears in the image, can be computed by normalizing H 1F .
  • the resulting normalized histogram will be referred to as H 1FN in the following.
  • the purpose of the second method part i.e., histogram analysis, is to identify a significant threshold (Th Bone ), which allows separating the brighter bone from the darker soft tissue and background pixels. It is not possible to assign a pre-defined value to Th Bone because the levels of the two families of tissues, and consequently Th Bone , vary from image to image, depending on the subject's anatomical characteristics.
  • Th Bone is based on mixture models. These form the basis of powerful statistical techniques for density estimation in which the advantages of both parametric and non-parametric methods are combined.
  • Mixture models as such are known. For example, they are described in a general context in the book Neural Networks for Pattern Recognition by Christopher M. Bishop, Clarendon Press, Oxford, 1995, pp. 59-73, and in the book Finite Mixture Models by Geoffrey McLachlan and David Peel, Wiley, 2000. The disclosure of these books is herewith incorporated into the present document in its entirety.
  • Mixture models can generally estimate probability densities with complex shapes, such as multimodal histograms, like the one here, using a restricted number of parameters.
  • a mixture distribution is defined as a linear combination of M component densities p ( x
  • j ) dx 1
  • the probability density p (x) is generated as follow: first a component j is chosen with probability P ( j ); then a data point is generated from the component density p ( x
  • Posterior probabilities can be expressed using Bayes' theorem as P ( j
  • x ) p ( x
  • j ) P ( j ) p ( x ) with ⁇ j 1 M P ( j
  • x ) 1 where P ( j
  • a mixture of two Gaussians and one inverted Lognormal is used to model H 1FN .
  • Each component of this mixture takes into account the spread of the gray levels associated respectively to background, soft tissue and bone; the characteristic shape of the inverted Lognormal is used to properly describe the asymmetric shape of the bone peak.
  • the mixture model is completely defined as soon as the parameters of each distribution ( ⁇ j , ⁇ j ) and the three mixing parameters P ( j ) have been computed,
  • the inventors have obtained the following updating equations for use with the particular model of the present sample embodiment.
  • x n ) x n ⁇ n 1 N P old ( j
  • x n ) ( x n - ⁇ j ) 2 ⁇ n 1 N P old ( j
  • x n ) In ( N GL - x n ) ⁇ n 1 N P old ( j
  • x n ) ⁇ [ In ( N GL - x n ) - ⁇ j new ] 2 ⁇ n 1 N P old ( j
  • Equations (X13) - (X17) require that each pixel x is examined, leading to a large computational time.
  • the possible values of x are discrete (N GL values), and all the pixels having the same gray value have already been counted in the histogram H 1F .
  • the updating equations (X13) - (X17) can therefore be simplified.
  • the parameters are initialized to a mean value that has been pre-computed on the basis of a set of typical test images.
  • the above updating operations are now applied a sufficient number of times.
  • the 1 st Gaussian component of the histogram model corresponds to the background;
  • the 2 nd Gaussian component is associated with the soft tissue; and
  • the inverted Lognormal 3 rd component describes the bone's typical gray levels.
  • Th Bone The threshold that separates the soft tissue from the bone structures, Th Bone , is now set so that the following function is minimized: ⁇ 0 Th Bone p ( x
  • FIG. 4a - Fig. 4h illustrate the process described above.
  • the normalized histogram of the original image, H 1FN is plotted with a continuous line ( ⁇ ).
  • the probability density of each gray level, computed by the mixture model is plotted with a dash-dot line (- ⁇ - ⁇ - ⁇ - ⁇ ), and the probability densities of the three components are plotted with dotted lines ( ⁇ ).
  • the computed boundary value Th Bone is shown in each diagram as a vertical dashed line.
  • the histogram curves and the boundary value Th Bone are shown at iteration step 1 (Fig. 4a), iteration step 5 (Fig. 4b), iteration step 30 (Fig. 4c), and iteration step 100 (Fig. 4d) of the EM algorithm.
  • Fig. 4e depicts the fitting of the histogram through a mixture of three Gaussians.
  • Fig. 4f shows the fitting through a mixture of two Gaussians and one Inverted Poisson.
  • Fig. 4g illustrates the negative log-likelihood E according to equation (12), which has been normalized to its initial value, versus the number of iteration steps for eighteen typical radiographies.
  • the respective values of Th Bone and G Max for these eighteen radiographies are plotted in Fig. 4h.
  • the embodiment described above used a rather complex model with a mixture of statistical distributions for the three categories background, soft tissue and bone.
  • the complexity of this model may be a problem in some circumstances.
  • a simplified model will be described that only distinguishes two components of the histogram.
  • the feature categories corresponding to these two components are soft tissue (including background) on the one hand and bone on the other hand.
  • the model histogram of the embodiment described in the present section is not a mixture of statistical distributions, but is formed by segments of simple functions. This allows calculation of the boundary value Th Bone in a simplified process.
  • Fig. 5 shows the working histogram H 1 F (x) plotted as a continuous line ( ⁇ ).
  • the simplified histogram model is shown as a dashed line with uniform short dashes (-----).
  • the histogram model consists of a left-hand segment, which is just a line segment, and a right-hand segment, which is a piece of a parabola. Both segments join at a joining point, which is shown as a spot in Fig. 5.
  • the x coordinate (abscissa) of the joining point represents the boundary value Th Bone that is to be estimated; this value will be called G Threshold in the following.
  • Equation (S1) is an additional condition, which allows closing the system.
  • the value of E is computed for all the gray levels (x values) between G min and G max . Then, G threshold is determined as the value of x for which the corresponding E(x) is minimum.
  • the dashed line with alternating short and long dashes ( ⁇ - ⁇ - ⁇ ) in Fig. 5 shows the value of E(x) over the possible range of x values. It is apparent that the error E(x) reaches its minimum for x ⁇ 205, and consequently G threshold will be set to 205 in this particular run of the image enhancement method. This value will be used as the boundary value Th Bone in the next part of the method, namely the image correction.
  • the radiographic image is corrected in order to increase the visibility of its bone and soft tissue features.
  • ⁇ values are smoothed in the spatial domain to avoid strong artifacts like those shown in Fig. 6e.
  • FIG. 2a An example of an embodiment that includes a step of smoothing the gamma correction map is shown in Fig. 2a and described in the following.
  • a binarized gamma correction map, ⁇ b (.) is created.
  • the gamma correction map ⁇ b (.) contains either the value ⁇ Soft_tissue or the value ⁇ Bone .
  • Fig. 6b shows an example of a binarized gamma correction map ⁇ b (.), which has been obtained from the cephalic radiographic image shown in Fig. 6a.
  • the binarized gamma correction map ⁇ b (.) After the binarized gamma correction map ⁇ b (.) has been obtained, it is smoothed by a spatial filtering process to obtain the final gamma map, ⁇ f (.), which will be applied to the image.
  • the spatial filtering process starts with the step of down-sampling ⁇ b (.) into a downsampled map ⁇ d (.).
  • ⁇ b (.) is subdivided into square blocks, B l,m , of size TP x TP, where TP is a pre-set parameter of the image correction method.
  • the downsampled map ⁇ d (l,m) contains the mean gamma value inside the block B l,m .
  • the downsampled map ⁇ d (.) is then spatially filtered by using a 3x3 moving average; the result is shown in Fig. 6c.
  • This procedure is equivalent to an undersampling of ⁇ b (.) using partially overlapping windows.
  • the final gamma map ⁇ f (.) is then obtained by upsampling ⁇ d (.) through a bilinear interpolation scheme; Fig. 6d shows an example of the results, i.e., the final gamma map ⁇ f (.) that will be used for image correction.
  • a further step is performed in the present sample embodiment to take advantage of the full dynamics of the gray levels.
  • This step is that a linear stretching with saturation is carried out on the histogram H 1F before performing local gamma correction.
  • equation (22) is directly applied to each pixel.
  • a look-up table (LUT) is used for the image correction.
  • Equing equation (22) through the LUT provides for particularly fast processing times, i.e., interactive image generation rates.
  • the final gamma map ⁇ f (i,j) is discretized into N v values, ⁇ 0 , ..., F NV-1 .
  • N v values ⁇ 0 , ..., F NV-1 .
  • the corrected gray level, I'p is computed through equation (22) and stored in the LUT.
  • the LUT therefore is two-dimensional with a total of N GL x N v entries.
  • Fig. 7a and Fig. 7c represent two examples of original lateral cephalic radiographies.
  • Fig. 7e and Fig. 7f show the original and processed versions, respectively, of an overexposed lateral cephalic radiography.
  • the contrast between bone and soft tissue is quite high, but the soft tissue tends to merge with the background. Processing the image according to the method described above had the effect that the bone structures became more visible, while chin and soft tissue were clearly distinguishable from the background (Fig. 7f).
  • Fig. 7g shows an example of an underexposed lateral cephalic radiography.
  • ⁇ Bone 0,15
  • ⁇ Soft_Tissue 1
  • the soft tissue is visible, but the bone dynamic is very compressed; moreover, the contrast between soft tissue and bone is very low.
  • Fig. 7i and Fig. 7j demonstrate the effect of the method described above for a frontal cephalic radiography.
  • Total processing time for the method was about one second for each image, on average.
  • processing time, Tp was 1,08 ⁇ 0,01 s (mean ⁇ 2 standard deviations): only 6% of Tp is devoted to histogram computation and its analysis through the mixture model. Construction of ⁇ b , its smoothing to obtain ⁇ f , and gray level correction using the LUT, take 17%, 42% and 17% of Tp, respectively. The remaining 18% of T P is required by memory allocation. These times allow working at interactive rates and allow adjusting the parameters ( ⁇ Bone , ⁇ Soft_Tissue and TP) to obtain the subjectively optimal result.
  • the H N values (mean normalized entropy ⁇ 2 standard deviations) for a set of eighteen typical radiographies are shown in Fig. 8.
  • the two background distributions (peaks 2 and 3 in Fig. 3a) have been represented by a single Gaussian distribution in the model of section 2, but alternative embodiments are envisaged in which the background is modeled with two separate components, e.g., two Gaussian distributions.
  • the inverted Lognormal distribution as used in the model described above in section 2, has proved to work properly, both for underexposed radiographies (which have a narrow bone peak) and for overexposed images (which have a more widely spread histogram).
  • the three components of the model of section 2 provide an excellent representation of background, soft and bony tissue, as shown in Fig. 4d. This mixture is validated also by the quantitative comparison of the respective likelihood values obtained from the three mixtures.
  • the negative log likelihood value of the mixture composed of two Gaussians and one inverted Lognormal is normalized to 1, then the corresponding value for the mixture of three Gaussians is 1,0120 ⁇ 0,0105 (mean ⁇ 2 standard deviations), and the value for the mixture of two Gaussians and one inverted Poisson is 1,0136 ⁇ 0,0124 for the eighteen test images.
  • the analytical shape of the inverted Lognormal distribution according to equation (8) is particularly advantageous because of the possibility to derive closed analytical equations to update the parameters.
  • This form is suitable for use with the EM method, which produces a fast and stable convergence: less than 60 ms are required to perform 100 iterations.
  • ⁇ Soft_Tissue Three parameters, namely ⁇ Soft_Tissue , ⁇ Bone and TP, are used in the method described above.
  • This default setting yields good results over a wide variety of images, as demonstrated in Fig. 7a - Fig. 7d.
  • the user can modify the values of these parameters in an interactive way to obtain the best subjective image quality.
  • the gamma map is smoothed in the spatial domain to avoid strong artifacts (Fig. 6e).
  • the proposed four step procedure leads to a bilinear interpolation scheme to generate ⁇ f . It is envisaged to use a spline-based upsampling in alternative embodiments, but then additional measures need to be taken to avoid oscillations in ⁇ f .
  • smoothing of the binary map ⁇ b is performed by a simple moving average filter, which can be efficiently implemented in the spatial domain to work in real time. This simple solution, however, tends to generate artifacts in ⁇ f due to the square shape of the moving window. More refined techniques, in which the filter scale is selected locally on the basis of scale-space analysis, are also envisaged and may be used in future developments where more computational power is available.
  • the Soft Tissue Filtering method described in the present document corresponds to a local monotonic non-linear stretching of the gray scale, where soft tissue and bone gray level ranges are enlarged to make the structures more visible. As a result, the histogram of bone and soft tissue partially overlaps. This is not a problem for the primary fields of use envisaged for the present invention, where a clinician needs to perform precise identification of anatomical features, alterations in the anatomy of a patient, and visualization of post-operative aesthetic modifications.
  • the core of the embodiment described in sections 1, 2 and 4 is an innovative modeling of the histogram through an adequate mixture model, which allows a reliable clustering of the cephalic images in a very short time (less than 60 ms).
  • the image enhancement method of the present invention constitutes a powerful tool for clearly visualizing both soft tissue and bone in the same image. Moreover, the image enhancement method can be integrated in tools for automatic cephalometric orthodontia. The speed of operation and the intuitive modification of the free parameters are important benefits.
  • the approach described in the present document can be adapted to all types of medical images that have a well defined multi-modal histogram, and the approach can be used in all medical fields where features of different tissues in a single image need to be clearly visualized.

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ES2453415A1 (es) * 2013-12-18 2014-04-07 Universidad De León Procedimiento y sistema para la estimación de la proporción de espermatozoides presentes en una muestra que pertenecen a una clase determinada
ES2465740A1 (es) * 2013-09-24 2014-06-06 Universidad De León Procedimiento de visión artificial para la detección de gotas citoplasmáticas proximales en espermatozoides

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